Centaurea corymbosa is an endemic plant species of La Clape, an region in Southern France.
It is closely related to other Centaurea species found in the region, but unlike C. corymbosa, these are very abundant.
C. corymbosa
22 February, 2021
Centaurea corymbosa is an endemic plant species of La Clape, an region in Southern France.
It is closely related to other Centaurea species found in the region, but unlike C. corymbosa, these are very abundant.
C. corymbosa
Is Centaurea corymbosa threatened because it is not as competitive as other Centaurea species?
For each treatment, 50 replicates were seeded for each population and each species, for a total of 800 seeds monitored at different time intervals: each week for 3 weeks, and then once per month*.
| C.c. (Pop1) | C.c. (Pop2) | C.m. (Pop3) | C.m. (Pop4) | |
|---|---|---|---|---|
| Control | C.c. (Pop1)/Control | C.c. (Pop2)/Control | C.m. (Pop3)/Control | C.m. (Pop4)/Control |
| Not Dense | C.c. (Pop1)/Not Dense | C.c. (Pop2)/Not Dense | C.m. (Pop3)/Not Dense | C.m. (Pop4)/Not Dense |
| Dense | C.c. (Pop1)/Dense | C.c. (Pop2)/Dense | C.m. (Pop3)/Dense | C.m. (Pop4)/Dense |
| Very Dense | C.c. (Pop1)/Very Dense | C.c. (Pop2)/Very Dense | C.m. (Pop3)/Very Dense | C.m. (Pop4)/Very Dense |
# Format the date of germination correctly
data$Date_de_germination <- as.Date(data$Date_de_germination,
format = "%d/%m/%Y")
head(data[1:3,1:5])
## # A tibble: 3 x 5 ## Plante Date_de_germination Cotyledons Taille_Dec_05 Taille_Fev_06 ## <dbl> <date> <dbl> <dbl> <dbl> ## 1 1 NA NA 0 0 ## 2 2 NA NA 0 0 ## 3 3 NA NA 0 0
1st decision - We are not interested in the rosette size of plants that do not germinate, so set to NA
# If plant didn't germinate, it should not have a rosette size
data_nogerm <- data %>%
filter(is.na(Date_de_germination)) %>%
mutate(Taille_Dec_05 = NA,
Taille_Fev_06 = NA,
Taille_Mars_06 = NA,
Taille_Juin_06 = NA,
Taille_Sept_06 = NA)
data_germ <- data %>%
filter(!is.na(Date_de_germination))
fulldata <- full_join(data_nogerm, data_germ)
## Joining, by = c("Plante", "Date_de_germination", "Cotyledons", "Taille_Dec_05", "Taille_Fev_06", "Taille_Mars_06", "Taille_Juin_06", "Taille_Sept_06", "traitement", "PAR", "reference", "espece", "pop")
2nd decision: We are interested in the size of the rosette over time, as this will be our indication of how well each plant is coping with the competition.
gathered_data <- fulldata %>%
rename("2005-12-01" = "Taille_Dec_05",
"2006-02-01" = "Taille_Fev_06",
"2006-03-01" = "Taille_Mars_06",
"2006-06-01" = "Taille_Juin_06",
"2006-09-01" = "Taille_Sept_06") %>%
gather(key = "Date",
value = "Rosette_size",
"2005-12-01":"2006-09-01")
gathered_data$Date <- as.Date(gathered_data$Date, format = "%Y-%m-%d")
3rd decision: To make sense of the light data, we divide the PAR received by the plant through the Brachypodium by the max PAR that pot was receiving (actual/max possible). As the max values were taken for areas within the greenhouse (presumed), not each pot, some values of actual/max are slightly above 1.
gathered_data <- gathered_data %>% mutate(light = PAR/reference)
Is light received (actual PAR / Max PAR) perhaps a better measure of competition than the visual sorting of % soil covered?
Something is weird: lack of 0s in the June measurements